class_docstr: multipledispatch.Dispatcher uses a generator to yield the
  desired function implementation, which is problematic as TensorFlow's
  autograph is not able to compile code that passes through generators.
  
  This class overwrites the problematic method in the original
  Dispatcher and solely makes use of simple for-loops, which are
  compilable by AutoGraph.
code: def register(
      self, *types: Types, **kwargs: Any
  ) -> Callable[[_C], _C]:
      # Override to add type hints...
      result: Callable[[_C], _C] = super().register(*types, **kwargs)
      return result
  
file_path: GPflow/gpflow/utilities/multipledispatch.py
imports: from typing import Any, Callable, Optional, Tuple, Type, TypeVar, Union
  from multipledispatch import Dispatcher
  from multipledispatch.dispatcher import str_signature, variadic_signature_matches
  from multipledispatch.variadic import isvariadic
label_desc: Use image node_id 1 for calling the Dispatcher obj's underlying member method code with example usage: obj.register() and returns: result